| """Training schema; labels/provenance never participate in model rendering.""" |
|
|
| import math |
| from typing import Any, Literal |
|
|
| from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, model_validator |
|
|
| from decision_schema import Question, options |
|
|
| QUESTION = TypeAdapter(Question) |
| KINDS = ("choice", "score", "noul") |
| MODELS = ( |
| "github-copilot/gpt-6-luna", |
| "github-copilot/grok-4.7", |
| "github-copilot/gpt-6-sol", |
| ) |
|
|
|
|
| def validate_question(value): |
| q = QUESTION.validate_python(value).model_dump(exclude_none=True) |
| if not q.get("instructions"): |
| raise ValueError("instructions required") |
| if q["type"] in {"choice", "score"} and len(q["criteria"]) < 2: |
| raise ValueError("training requires at least two candidates") |
| return q |
|
|
|
|
| def label_key(label, kind): |
| if kind == "noul": |
| if isinstance(label, bool): |
| return "true" if label else "false" |
| if label in ("true", "false"): |
| return label |
| raise ValueError("noul label must be a boolean or true/false string") |
| if isinstance(label, bool): |
| raise ValueError("boolean is not a choice/score label") |
| return str(label) |
|
|
|
|
| class Target(BaseModel): |
| model_config = ConfigDict(extra="forbid") |
| hard_label: str | int | bool |
| evidence: list[dict[str, Any]] = Field(min_length=1) |
| ambiguity: Literal["none", "ambiguous", "insufficient", "conflict"] = "none" |
|
|
|
|
| class Case(BaseModel): |
| model_config = ConfigDict(extra="forbid") |
| case_id: str |
| group_id: str |
| domain: str |
| task_family: str |
| language: Literal["zh", "en", "mixed"] |
| state: str | dict[str, Any] | list[Any] |
| questions: dict[str, dict[str, Any]] = Field(min_length=1, max_length=8) |
| targets: dict[str, Target] |
|
|
| @model_validator(mode="after") |
| def check_targets(self): |
| if set(self.questions) != set(self.targets): |
| raise ValueError("question/target IDs differ") |
| for qid, question in self.questions.items(): |
| q = validate_question(question) |
| self.questions[qid] = q |
| key = label_key(self.targets[qid].hard_label, q["type"]) |
| if key not in [k for k, _ in options(q)]: |
| raise ValueError("target is not a candidate") |
| return self |
|
|
|
|
| def check_distribution(values): |
| if not values or any(not math.isfinite(x) or x < 0 for x in values): |
| raise ValueError("invalid distribution") |
| if abs(sum(values) - 1) > 1e-5: |
| raise ValueError("probabilities must sum to one") |
|
|
